






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
SCREAMING_SNAKE_CASE__		:							Tuple 				=				""""""
SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				""""""
SCREAMING_SNAKE_CASE__		:							List[Any] 				=				""""""
SCREAMING_SNAKE_CASE__		:							Tuple 				=				1  # (0 is vertical, 1 is horizontal)





def  _lowerCamelCase						(						) ->			Union[str, Any]:

     '''simple docstring'''
     UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			get_dataset(lowercase_ ,							lowercase_       )
     print("""Processing..."""       )
     UpperCAmelCase__      ,		UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			update_image_and_anno(lowercase_ ,							lowercase_ ,							lowercase_       )

     for index, image in enumerate(lowercase_       ):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          UpperCAmelCase__      :  str				      =			random_chars(32       )
          UpperCAmelCase__      :  int				      =			paths[index].split(os.sep       )[-1].rsplit(""".""" ,							1       )[0]
          UpperCAmelCase__      :  Union[str, Any]				      =			F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
          cva.imwrite(F"/{file_root}.jpg" ,							lowercase_ ,							[cva.IMWRITE_JPEG_QUALITY, 85]       )
          print(F"Success {index+1}/{len(lowercase_       )} with {file_name}"       )
          UpperCAmelCase__      :  Tuple				      =			[]
          for anno in new_annos[index]:
               UpperCAmelCase__      :  List[str]				      =			F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
               annos_list.append(lowercase_       )
          with open(F"/{file_root}.txt" ,							"""w"""       ) as outfile:
               outfile.write("""\n""".join(line for line in annos_list       )       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			[]
     UpperCAmelCase__      :  Optional[Any]				      =			[]
     for label_file in glob.glob(os.path.join(lowercase_ ,							"""*.txt"""       )       ):
          UpperCAmelCase__      :  List[Any]				      =			label_file.split(os.sep       )[-1].rsplit(""".""" ,							1       )[0]
          with open(lowercase_       ) as in_file:
               UpperCAmelCase__      :  int				      =			in_file.readlines()
          UpperCAmelCase__      :  Dict				      =			os.path.join(lowercase_ ,							F"{label_name}.jpg"       )

          UpperCAmelCase__      :  int				      =			[]
          for obj_list in obj_lists:
               UpperCAmelCase__      :  List[Any]				      =			obj_list.rstrip("""\n"""       ).split(""" """       )
               boxes.append(
                   [
                       int(obj[0]       ),
                       float(obj[1]       ),
                       float(obj[2]       ),
                       float(obj[3]       ),
                       float(obj[4]       ),
                   ]       )
          if not boxes:
               continue
          img_paths.append(lowercase_       )
          labels.append(lowercase_       )
     return img_paths, labels





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase = 1       ) ->			int:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			[]
     UpperCAmelCase__      :  List[str]				      =			[]
     UpperCAmelCase__      :  Optional[Any]				      =			[]
     for idx in range(len(lowercase_       )       ):
          UpperCAmelCase__      :  Optional[Any]				      =			[]
          UpperCAmelCase__      :  List[Any]				      =			img_list[idx]
          path_list.append(lowercase_       )
          UpperCAmelCase__      :  List[str]				      =			anno_list[idx]
          UpperCAmelCase__      :  Any				      =			cva.imread(lowercase_       )
          if flip_type == 1:
               UpperCAmelCase__      :  Optional[int]				      =			cva.flip(lowercase_ ,							lowercase_       )
               for bbox in img_annos:
                    UpperCAmelCase__      :  Any				      =			1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]       )
          elif flip_type == 0:
               UpperCAmelCase__      :  List[Any]				      =			cva.flip(lowercase_ ,							lowercase_       )
               for bbox in img_annos:
                    UpperCAmelCase__      :  Any				      =			1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]       )
          new_annos_lists.append(lowercase_       )
          new_imgs_list.append(lowercase_       )
     return new_imgs_list, new_annos_lists, path_list





def  _lowerCamelCase						(						__lowerCamelCase = 32       ) ->			int:

     '''simple docstring'''
     assert number_char > 1, "The number of character should greater than 1"
     UpperCAmelCase__      :  List[Any]				      =			ascii_lowercase + digits
     return "".join(random.choice(lowercase_       ) for _ in range(lowercase_       )       )


if __name__ == "__main__":
      main()
      print("""DONE ✅""")














'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(
             UpperCamelCase__   ,	split=UpperCamelCase__   ,	features=UpperCamelCase__   ,	cache_dir=UpperCamelCase__   ,	keep_in_memory=UpperCamelCase__   ,	streaming=UpperCamelCase__   ,	num_proc=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
         a_						     =			field
         a_						     =			path_or_paths if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else {self.split: path_or_paths}
         a_						     =			Json(
             cache_dir=UpperCamelCase__   ,	data_files=UpperCamelCase__   ,	features=UpperCamelCase__   ,	field=UpperCamelCase__   ,	**UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         if self.streaming:
                a_						     =			self.builder.as_streaming_dataset(split=self.split	)
         # Build regular (map-style) dataset
         else:
                a_						     =			None
                a_						     =			None
                a_						     =			None
                a_						     =			None

                self.builder.download_and_prepare(
                    download_config=UpperCamelCase__   ,	download_mode=UpperCamelCase__   ,	verification_mode=UpperCamelCase__   ,	base_path=UpperCamelCase__   ,	num_proc=self.num_proc   ,	)
                a_						     =			self.builder.as_dataset(
                    split=self.split   ,	verification_mode=UpperCamelCase__   ,	in_memory=self.keep_in_memory	)
         return dataset





class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         if num_proc is not None and num_proc <= 0:
                raise ValueError(f'num_proc {num_proc} must be an integer > 0.'	)

         a_						     =			dataset
         a_						     =			path_or_buf
         a_						     =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         a_						     =			num_proc
         a_						     =			'utf-8'
         a_						     =			to_json_kwargs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.to_json_kwargs.pop('path_or_buf'   ,	UpperCamelCase__	)
         a_						     =			self.to_json_kwargs.pop('orient'   ,	'records'	)
         a_						     =			self.to_json_kwargs.pop('lines'   ,	True if orient == 'records' else False	)
         a_						     =			self.to_json_kwargs.pop('index'   ,	False if orient in ['split', 'table'] else True	)
         a_						     =			self.to_json_kwargs.pop('compression'   ,	UpperCamelCase__	)

         if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                raise NotImplementedError(f'`datasets` currently does not support {compression} compression'	)

         if isinstance(self.path_or_buf   ,	(str, bytes, os.PathLike)	):
                with fsspec.open(self.path_or_buf   ,	'wb'   ,	compression=UpperCamelCase__	) as buffer:
                       a_						     =			self._write(file_obj=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         else:
                if compression:
                       raise NotImplementedError(
                           f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                           ' was passed. Please provide a local path instead.'	)
                a_						     =			self._write(
                    file_obj=self.path_or_buf   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         return written







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_     ,	a_     ,	a_     ,	a_     ,	a_						     =			args

         a_						     =			query_table(
             table=self.dataset.data   ,	key=slice(UpperCamelCase__   ,	offset + self.batch_size	)   ,	indices=self.dataset._indices   ,	)
         a_						     =			batch.to_pandas().to_json(
             path_or_buf=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**UpperCamelCase__	)
         if not json_str.endswith('\n'	):
                json_str += "\n"
         return json_str.encode(self.encoding	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			0

         if self.num_proc is None or self.num_proc == 1:
                for offset in logging.tqdm(
                    range(0   ,	len(self.dataset	)   ,	self.batch_size	)   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                       a_						     =			self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                       written += file_obj.write(UpperCamelCase__	)
         else:
                a_     ,	a_						     =			len(self.dataset	), self.batch_size
                with multiprocessing.Pool(self.num_proc	) as pool:
                       for json_str in logging.tqdm(
                           pool.imap(
                               self._batch_json   ,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0   ,	UpperCamelCase__   ,	UpperCamelCase__	)]   ,	)   ,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                              written += file_obj.write(UpperCamelCase__	)

         return written




